This article introduces an online web-based real-time speech recognition tool that supports real-time microphone recording and speech recognition of audio/video files. It is free to use (without usage restrictions).

https://stt.pyvideotrans.com

Speech recognition technology, also known as speech-to-text, uses artificial intelligence to convert speech in audio or video into text. This technology has wide applications in many fields, such as meeting minutes, voice assistants, subtitle generation, and more.

Currently, there are two main methods of speech recognition:

1. Offline Model-Based Speech Recognition:

This method requires deploying a speech recognition model on a local computer. A popular open-source solution is OpenAI Whisper. After downloading its large model (e.g., large-v2), it can be used offline without needing an internet connection or payment.

However, this method requires strong computing resources (e.g., a powerful graphics card), otherwise the recognition speed will be slow and the accuracy will decrease.

2. Online API-Based Speech Recognition:

Some companies provide online speech recognition API services, such as ByteDance and OpenAI.

Users only need to upload audio data to the API to get the transcription results.

This method does not require local hardware resources, is fast and accurate, but requires a certain fee.

Real-time Speech Recognition

The above two methods are mainly for existing audio or video files. So, how can we perform real-time transcription of audio streams recorded live by a microphone? For example, how can we record speakers in real-time during a meeting and convert it into text?

Real-time speech recognition is similar in principle to file transcription, but it is more technically difficult. It requires:

• Real-time Data Stream Processing: Continuously receiving audio data from the microphone.
• Data Segmentation and Recognition: Dividing the continuous audio stream into smaller segments and recognizing them individually.
• Result Integration and Correction: Integrating the recognition results of each segment and correcting them to improve the accuracy of the final transcription. This usually requires more complex algorithms to handle speech pauses, overlaps, and other situations.
• Minimum Latency: Minimizing the delay from audio input to text output as much as possible to ensure real-time performance.

Technical Principles and Usage Introduction

• Microphone Real-time Recording Recognition: Records audio in real-time using a microphone and transcribes it in real-time.
• Audio and Video File Speech Recognition: Supports uploading local audio or video files for transcription.

Technical Principles:

1. Lightweight Speech Recognition Model (Vosk): In order to run in a browser environment, we have adopted the small and lightweight Vosk speech recognition model. Although its accuracy is relatively low, it can effectively reduce resource consumption and ensure smooth operation in the browser.

2. Local Audio Processing (ffmpeg.wasm): Uses ffmpeg.wasm to process audio and video files and extract speech within the user's browser, without uploading audio data to the server.

3. Client-side Model Loading: The speech recognition model is downloaded and run in the browser's memory. This limits us from using larger and more accurate models, and we can only choose smaller models to avoid browser crashes. Even if the user's computer has powerful performance, large models are not currently supported due to server bandwidth limitations.

How to Use

1. Model Loading: Before use, please load the Chinese or English model as needed.
2. Microphone Recognition: Click the button in the left area to start real-time recording and recognition using the microphone. The recognition results will be displayed in the text box in real-time.
3. File Recognition: Select a local audio or video file in the right area. The tool will use ffmpeg.wasm for local processing and speech recognition. The results are displayed in the text box.
4. Result Download: The transcribed text can be downloaded as a TXT file.

Precautions

1. Mutually Exclusive Functions: Microphone real-time recognition and file recognition functions cannot be used simultaneously.
2. Local Processing: Model and audio processing are performed locally in the user's browser.
3. Language Support: Currently only supports Chinese and English speech recognition.
4. Performance Limitations: Due to the use of a lightweight model, the recognition accuracy may not be as good as large models.

Common Problems

• Q: What if the recognition accuracy is low? A: We use a lightweight model to ensure browser compatibility and running speed. If you need higher accuracy, we recommend downloading pyVideoTrans and using the large-v2 model locally.
• Q: Which languages are supported? A: Currently only supports Chinese and English.
• Q: Why is it slow? A: This may be due to poor network conditions, browser performance, or insufficient computer resources.
• Q: How big of a file can I upload? A: File size is limited by browser memory and processing power.